{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The easiest way to download YouTube videos using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an article accompanying this Notebook, please go [here](https://towardsdatascience.com/the-easiest-way-to-download-youtube-videos-using-python-2640958318ab)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:05.053295Z",
     "start_time": "2020-02-16T18:12:04.758001Z"
    }
   },
   "outputs": [],
   "source": [
    "from pytube import YouTube\n",
    "\n",
    "# misc\n",
    "import os\n",
    "import shutil\n",
    "import math\n",
    "import datetime\n",
    "\n",
    "# plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# image operation\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading a video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the instance of the YouTube class\n",
    "video = YouTube('https://www.youtube.com/watch?v=NqC_1GuY3dw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary:\n",
      "Title: Game Boy Longplay [009] Mega Man: Dr. Wily's Revenge\n",
      "Duration: 39.80 minutes\n",
      "Rating: 4.87\n",
      "# of views: 164106\n"
     ]
    }
   ],
   "source": [
    "# print a summary of the selected video\n",
    "print('Summary:')\n",
    "print(f'Title: {video.title}')\n",
    "print(f'Duration: {video.length / 60:.2f} minutes')\n",
    "print(f'Rating: {video.rating:.2f}')\n",
    "print(f'# of views: {video.views}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Stream: itag=\"18\" mime_type=\"video/mp4\" res=\"360p\" fps=\"30fps\" vcodec=\"avc1.42001E\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">,\n",
       " <Stream: itag=\"133\" mime_type=\"video/mp4\" res=\"240p\" fps=\"30fps\" vcodec=\"avc1.4d400d\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"242\" mime_type=\"video/webm\" res=\"240p\" fps=\"30fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"160\" mime_type=\"video/mp4\" res=\"144p\" fps=\"30fps\" vcodec=\"avc1.4d400c\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"278\" mime_type=\"video/webm\" res=\"144p\" fps=\"30fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"140\" mime_type=\"audio/mp4\" abr=\"128kbps\" acodec=\"mp4a.40.2\" progressive=\"False\" type=\"audio\">,\n",
       " <Stream: itag=\"251\" mime_type=\"audio/webm\" abr=\"160kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display all streams\n",
    "video.streams.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Stream: itag=\"18\" mime_type=\"video/mp4\" res=\"360p\" fps=\"30fps\" vcodec=\"avc1.42001E\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">,\n",
       " <Stream: itag=\"133\" mime_type=\"video/mp4\" res=\"240p\" fps=\"30fps\" vcodec=\"avc1.4d400d\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"160\" mime_type=\"video/mp4\" res=\"144p\" fps=\"30fps\" vcodec=\"avc1.4d400c\" progressive=\"False\" type=\"video\">,\n",
       " <Stream: itag=\"140\" mime_type=\"audio/mp4\" abr=\"128kbps\" acodec=\"mp4a.40.2\" progressive=\"False\" type=\"audio\">]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display only the streams with the selected file format \n",
    "video.streams.filter(file_extension = \"mp4\").all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/eryklewinson/Eryk_private_stuff/Git/medium_articles/Game Boy Longplay [009] Mega Man Dr Wilys Revenge.mp4'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download the selected video\n",
    "video.streams.get_by_itag(18).download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract frames from the video "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:07.662537Z",
     "start_time": "2020-02-16T18:12:07.651952Z"
    }
   },
   "outputs": [],
   "source": [
    "class FrameExtractor():\n",
    "    '''\n",
    "    Class used for extracting frames from a video file.\n",
    "    '''\n",
    "    def __init__(self, video_path):\n",
    "        self.video_path = video_path\n",
    "        self.vid_cap = cv2.VideoCapture(video_path)\n",
    "        self.n_frames = int(self.vid_cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "        self.fps = int(self.vid_cap.get(cv2.CAP_PROP_FPS))\n",
    "        \n",
    "    def get_video_duration(self):\n",
    "        '''Method for printing the video's duration'''\n",
    "        duration = self.n_frames/self.fps\n",
    "        print(f'Duration: {datetime.timedelta(seconds=duration)}')\n",
    "        \n",
    "    def get_n_images(self, every_x_frame):\n",
    "        '''\n",
    "        Method for calculating the expected number of images to save given \n",
    "        we save every x-th frame\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        \n",
    "        every_x_frame : int\n",
    "            Indicates we want to look at every x-th frame\n",
    "        '''\n",
    "        n_images = math.floor(self.n_frames / every_x_frame) + 1\n",
    "        print(f'Extracting every {every_x_frame} (nd/rd/th) frame would result in {n_images} images.')\n",
    "        \n",
    "    def extract_frames(self, every_x_frame, img_name, dest_path=None, img_ext = '.jpg'):\n",
    "        '''\n",
    "        Method used for extracting the frames from images\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        \n",
    "        every_x_frame : int\n",
    "            Indicates we want to extract every x-th frame\n",
    "        img_name : str\n",
    "            The image name, numbers will be appended (after an underscore) at the end\n",
    "        dest_path : str\n",
    "            The path where to store the images. Default (None) saves the images to current directory.\n",
    "        img_ext : str\n",
    "            Indicates the desired extension of the image. Default is JPG\n",
    "        '''\n",
    "        if not self.vid_cap.isOpened():\n",
    "            self.vid_cap = cv2.VideoCapture(self.video_path)\n",
    "        \n",
    "        if dest_path is None:\n",
    "            dest_path = os.getcwd()\n",
    "        else:\n",
    "            if not os.path.isdir(dest_path):\n",
    "                os.mkdir(dest_path)\n",
    "                print(f'Created the following directory: {dest_path}')\n",
    "        \n",
    "        frame_cnt = 0\n",
    "        img_cnt = 0\n",
    "\n",
    "        while self.vid_cap.isOpened():\n",
    "            \n",
    "            success,image = self.vid_cap.read() \n",
    "            \n",
    "            if not success:\n",
    "                break\n",
    "            \n",
    "            if frame_cnt % every_x_frame == 0:\n",
    "                img_path = os.path.join(dest_path, ''.join([img_name, '_', str(img_cnt), img_ext]))\n",
    "                cv2.imwrite(img_path, image)  \n",
    "                img_cnt += 1\n",
    "                \n",
    "            frame_cnt += 1\n",
    "        \n",
    "        self.vid_cap.release()\n",
    "        cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:08.374670Z",
     "start_time": "2020-02-16T18:12:08.356574Z"
    }
   },
   "outputs": [],
   "source": [
    "# instantiate the class using the downloaded video\n",
    "fe = FrameExtractor('Game Boy Longplay [009] Mega Man Dr Wilys Revenge.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:08.797626Z",
     "start_time": "2020-02-16T18:12:08.792649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71650"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the number of frames in the video\n",
    "fe.n_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:09.290171Z",
     "start_time": "2020-02-16T18:12:09.287150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration: 0:39:48.333333\n"
     ]
    }
   ],
   "source": [
    "# print the video's duration\n",
    "fe.get_video_duration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:09.785784Z",
     "start_time": "2020-02-16T18:12:09.782373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting every 1000 (nd/rd/th) frame would result in 71 images.\n"
     ]
    }
   ],
   "source": [
    "# calculate the potential number of frames \n",
    "fe.get_n_images(every_x_frame=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:24.274399Z",
     "start_time": "2020-02-16T18:12:11.356489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created the following directory: megaman_images\n"
     ]
    }
   ],
   "source": [
    "# extract every 1000th frame\n",
    "fe.extract_frames(every_x_frame=1000, \n",
    "                  img_name='megaman', \n",
    "                  dest_path='megaman_images')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:12:24.309862Z",
     "start_time": "2020-02-16T18:12:24.306082Z"
    }
   },
   "outputs": [],
   "source": [
    "# define a function for viewing images and test it\n",
    "def show_image(path):\n",
    "    image = cv2.imread(path)\n",
    "    plt.imshow(image)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T18:16:13.481114Z",
     "start_time": "2020-02-16T18:16:13.347973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image('megaman_images/megaman_61.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3.7.3 64-bit ('anaconda3': virtualenv)",
   "language": "python",
   "name": "python37364bitanaconda3virtualenvf7a65802c75648f3a92879e17a01f14c"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  },
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
